The encoder-decoder architecture used for NMT consists of two recurrent neural networks (RNN), one for the encoder and the other for the decoder. The encoder maps a source sequence into a continuous space representation and the decoder maps the representation back to a target sequence.
Our trained neural translation models are based on a bidirectional encoder-decoder deep neural network equipped with an attention mechanism [1]}, as described in Figure .
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